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An open-source device for measuring food intake and operant An open-source device for measuring food intake and operant

behavior in rodent home-cages behavior in rodent home-cages

Bridget A. Matikainen-Ankney Washington University School of Medicine in St. Louis

Thomas Earnest Washington University School of Medicine in St. Louis

Eric Casey Washington University School of Medicine in St. Louis

Justin G. Wang Washington University School of Medicine in St. Louis

Alex A. Legaria Washington University School of Medicine in St. Louis

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Recommended Citation Recommended Citation Matikainen-Ankney, Bridget A.; Earnest, Thomas; Casey, Eric; Wang, Justin G.; Legaria, Alex A.; Barclay, Kia M.; Norris, Makenzie R.; Chang, Yu-Hsuan; Lin, Eric; Conway, Sineadh M.; Al-Hasani, Ream; McCall, Jordan G.; Creed, Meaghan C.; Kravitz, Alexxai V.; and et al, ,"An open-source device for measuring food intake and operant behavior in rodent home-cages." Elife. 10,. . (2021). https://digitalcommons.wustl.edu/open_access_pubs/10246

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Authors Authors Bridget A. Matikainen-Ankney, Thomas Earnest, Eric Casey, Justin G. Wang, Alex A. Legaria, Kia M. Barclay, Makenzie R. Norris, Yu-Hsuan Chang, Eric Lin, Sineadh M. Conway, Ream Al-Hasani, Jordan G. McCall, Meaghan C. Creed, Alexxai V. Kravitz, and et al

This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/10246

*For correspondence:

alexxai@wustl.edu

†These authors contributed

equally to this work

Competing interest: See

page 14

Funding: See page 15

Received: 04 January 2021

Accepted: 26 March 2021

Published: 29 March 2021

Reviewing editor: Denise Cai,

Icahn School of Medicine at

Mount Sinai, United States

This is an open-access article,

free of all copyright, and may be

freely reproduced, distributed,

transmitted, modified, built

upon, or otherwise used by

anyone for any lawful purpose.

The work is made available under

the Creative Commons CC0

public domain dedication.

An open-source device for measuringfood intake and operant behavior inrodent home-cagesBridget A Matikainen-Ankney1†, Thomas Earnest1†, Mohamed Ali2,3†, Eric Casey1,Justin G Wang4, Amy K Sutton2, Alex A Legaria4, Kia M Barclay4,Laura B Murdaugh5, Makenzie R Norris4,6, Yu-Hsuan Chang4, Katrina P Nguyen2,Eric Lin1, Alex Reichenbach7, Rachel E Clarke7, Romana Stark7,Sineadh M Conway6,8, Filipe Carvalho9, Ream Al-Hasani6,8, Jordan G McCall6,8,Meaghan C Creed1,4,8, Victor Cazares10, Matthew W Buczynski5,Michael J Krashes2, Zane B Andrews7, Alexxai V Kravitz1,4,8*

1Department of Psychiatry, Washington University in St. Louis, St. Louis, UnitedStates; 2National Institute of Diabetes and Digestive and Kidney Diseases,Bethesda, United States; 3Department of Bioengineering, University of Maryland,College Park, United States; 4Department of Neuroscience, Washington Universityin St. Louis, St. Louis, United States; 5Department of Neuroscience, VirginiaPolytechnic and State University, Blacksburg, United States; 6Center for ClinicalPharmacology, University of Health Sciences and Pharmacy, St. Louis, United States;7Department of Physiology, Monash University, Clayton, Australia; 8Department ofAnesthesiology, Washington University in St. Louis, St. Louis, United States; 9OpenEphys Production Site, Lisbon, Portugal; 10Department of Psychology, WilliamsCollege, Williamstown, United States

Abstract Feeding is critical for survival, and disruption in the mechanisms that govern food

intake underlies disorders such as obesity and anorexia nervosa. It is important to understand both

food intake and food motivation to reveal mechanisms underlying feeding disorders. Operant

behavioral testing can be used to measure the motivational component to feeding, but most food

intake monitoring systems do not measure operant behavior. Here, we present a new solution for

monitoring both food intake and motivation in rodent home-cages: the Feeding Experimentation

Device version 3 (FED3). FED3 measures food intake and operant behavior in rodent home-cages,

enabling longitudinal studies of feeding behavior with minimal experimenter intervention. It has a

programmable output for synchronizing behavior with optogenetic stimulation or neural

recordings. Finally, FED3 design files are open-source and freely available, allowing researchers to

modify FED3 to suit their needs.

IntroductionFeeding is critical for survival, and dysregulation of food intake underlies medical conditions such as

obesity and anorexia nervosa. Quantifying food intake is necessary for understanding these disor-

ders in animal models. However, it is challenging to accurately measure food intake in rodents due

to the small volume of food that they eat. Researchers have devised multiple methods for quantify-

ing food intake in rodents, each with advantages and drawbacks (Ali and Kravitz, 2018). Manual

weighing of a food container is a simple and widely used method for quantifying food intake in a

rodent home-cage. Yet this is time consuming to complete, is subject to error and variability, and

Matikainen-Ankney, Earnest, Ali, et al. eLife 2021;10:e66173. DOI: https://doi.org/10.7554/eLife.66173 1 of 18

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does not allow for fine temporal analysis of consumption patterns (Acosta-Rodrıguez et al., 2017;

Reinert et al., 2019). Automated tools have been developed for measuring food intake in home-

cages with high temporal resolution, although most require modified caging, powdered foods, or

connected computers which limit throughput (Ahloy-Dallaire et al., 2019; Farley et al., 2003;

Moran, 2003; Yan et al., 2011). These include automated weighing (Hulsey and Martin, 1991;

Meguid et al., 1990; Minematsu et al., 1991), pellet dispensers (Aponte et al., 2011; Gill et al.,

1989; Oh et al., 2017), or video detection-based systems (Burnett et al., 2016; Jhuang et al.,

2010; Salem et al., 2015).

In addition to measuring total food intake, understanding neural circuits involved in feeding

requires exploring why animals seek and consume food. Has their motivation for a specific nutrient

changed? Has their feeding gained a compulsive nature that is insensitive to satiety signals? These

questions can be answered with operant tasks, where rodents receive food contingent on their

actions (Curtis et al., 2019; Mourra et al., 2020; O’Connor et al., 2015; Skinner, 1938; Thorn-

dike, 1898; Wald et al., 2020). Typically, operant behavior is tested in dedicated chambers for a

few hours each day. Commercial systems for testing operant behaviors typically comprise a dedi-

cated arena equipped with levers, food or liquid dispensers, tones or spotlights, and video cameras.

Programmable software interfaces allow experimenters to control this equipment to train animals on

behavioral tasks such as fixed-ratio or progressive ratio responding. Some chambers are also

equipped with video cameras and tracking software, so the location of the animal can be used as a

trigger to control task events (London et al., 2018). Training in dedicated operant chambers has lim-

itations: tasks can take weeks for animals to learn, animals may be tested at different phases of their

circadian cycle due to equipment availability, and food restriction can be necessary to get animals to

seek food outside of their home-cage, which can confound feeding studies. To mitigate these issues,

several researchers have begun to test operant behavior in rodent home-cages, resulting in both

fewer interventions from the researcher and faster rates of learning (Balzani et al., 2018;

Francis and Kanold, 2017; Lee et al., 2020).

eLife digest Obesity and anorexia nervosa are two health conditions related to food intake.

Researchers studying these disorders in animal models need to both measure food intake and

assess behavioural factors: that is, why animals seek and consume food.

Measuring an animal’s food intake is usually done by weighing food containers. However, this can

be inaccurate due to the small amount of food that rodents eat. As for studying feeding motivation,

this can involve calculating the number of times an animal presses a lever to receive a food pellet.

These tests are typically conducted in hour-long sessions in temporary testing cages, called operant

boxes. Yet, these tests only measure a brief period of a rodent’s life. In addition, it takes rodents

time to adjust to these foreign environments, which can introduce stress and may alter their feeding

behaviour.

To address this, Matikainen-Ankney, Earnest, Ali et al. developed a device for monitoring food

intake and feeding behaviours around the clock in rodent home cages with minimal experimenter

intervention. This ‘Feeding Experimentation Device’ (FED3) features a pellet dispenser and two

‘nose-poke’ sensors to measure total food intake, as well as motivation for and learning about food

rewards. The battery-powered, wire-free device fits in standard home cages, enabling long-term

studies of feeding behaviour with minimal intervention from investigators and less stress on the

animals. This means researchers can relate data to circadian rhythms and meal patterns, as

Matikainen-Ankney did here.

Moreover, the device software is open-source so researchers can customise it to suit their

experimental needs. It can also be programmed to synchronise with other instruments used in

animal experiments, or across labs running the same behavioural tasks for multi-site studies. Used in

this way, it could help improve reproducibility and reliability of results from such studies.

In summary, Matikainen-Ankney et al. have presented a new practical solution for studying food-

related behaviours in mice and rats. Not only could the device be useful to researchers, it may also

be suitable to use in educational settings such as teaching labs and classrooms.

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Here, we present a new solution for monitoring food intake and testing operant behavior in

rodent home-cages: the Feeding Experimentation Device version 3 (FED3). Our goal was to develop

a device for measuring food intake in rodent home-cages with high temporal resolution, while also

measuring food motivation via operant behavior. FED3 is a stand-alone device that contains a pellet

dispenser, two ‘nose-poke’ sensors for operant behavior, visual and auditory stimuli, and a screen

for experimenter feedback. FED3 is compact and battery powered, fitting in most commercial vivar-

ium home-cages without any connected computers or external wiring. FED3 also has a programma-

ble output that can control other equipment, for example to trigger optogenetic stimulation after a

nose-poke or pellet removal, or to synchronize feeding behavior with electrophysiological or fiber-

photometry recordings, or with in vivo calcium imaging. Finally, FED3 is open source and was

designed to be customized and re-programmed to perform novel tasks to help researchers under-

stand food intake and food motivation. To this end, we have written a user-friendly Arduino library

to facilitate custom behavioral programming of FED3. Here, we describe the design and construc-

tion of FED3 and present several experiments that demonstrate its functionality. These include mea-

suring circadian patterns of food intake over multiple days, performing meal pattern analysis,

automated operant training, closed-economy motivational testing, and optogenetic self-stimulation.

FED3 extends existing methods for quantifying food intake and operant behavior in rodents to help

researchers achieve a deeper understanding of feeding and feeding disorders.

Results

Hardware and design of the FED3FED3 is controlled by a 48 mHz ATSAMD21G18 ARM Cortex M0 microprocessor (Adafruit Feather

M0 Adalogger) and contains two nose-pokes, a pellet dispenser, a speaker for auditory stimuli, eight

multi-color LEDs for visual stimuli, a screen for experimenter feedback, and a programmable analog

output (Figure 1A,B). When mice interact with FED3, the timing of each poke and pellet removal

are logged to an internal microSD card for later analysis and summary data is displayed on the

screen for immediate feedback to the researcher (Figure 1B). FED3 is small (~10 cm � 12 cm � 9

cm), battery powered, and completely self-contained, so it fits in most vivarium home-cages without

modification or introducing wiring to the cage (Figure 1C). It is powered by a rechargeable battery

that lasts ~1 week between charges (exact battery life depends on the behavioral program being

Figure 1. Assembly of FED3. (A) Exploded view schematic and (B) photos of FED3 with main components

highlighted. (C) Assembled FED in an Allentown NextGen home-cage, top view, and side view. (D) Back view (left)

of assembled and populated FED3 PCB, and side view (right) of assembled FED3 electronics.

The online version of this article includes the following figure supplement(s) for figure 1:

Figure supplement 1. FED3 circuit.

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Tools and resources Neuroscience

run and number of pellets dispensed). FED3 also has magnetic mounts to facilitate wall-mounting on

any plastic box to mimic a traditional operant setup (see Video 1). We provide example programs

for running rodents on multiple common behavioral paradigms, including free feeding, time-

restricted free-feeding, fixed-ratio 1 (FR1), and progressive ratio (PR) operant tasks, and have written

a FED3 Arduino library to ease development of custom programs. FED3 also has an auxiliary I2C

port for hardware customization and a programmable BNC output connection that allows synchroni-

zation with external equipment for aligning behavioral events with fiber-photometry recordings

(London et al., 2018; Mazzone et al., 2020), electrophysiological recordings (London et al., 2018),

or other equipment such as video tracking systems (Krynitsky et al., 2020; Li et al., 2019). This

BNC output can report task events or control external hardware with delays of less than 200 ms,

which compares favorably with computers that process information through Universal Serial Bus

(USB), which can introduce delays of several milliseconds. Finally, we provide a graphical analysis

package (FED3VIZ) written in Python that enables users to generate detailed plots from FED3 data

(Figure 2F). FED3 is open-source and freely available online, including 3D design files, printed circuit

board (PCB) files (Figure 1—figure supplement 1A,B), build instructions (Figure 1D), and code

(https://github.com/KravitzLabDevices/FED3 copy archived at swh:1:rev:

ff8fc79d288a440d91566f4c6cd80011956f4be0).

Quantifying total food intakeTen FED3 devices were set to the ‘free-feeding’ program, which dispenses a pellet and monitors its

presence in the pellet well (Figure 2A). In this paradigm, each time the pellet is removed, FED3 logs

Figure 2. FED3 tracks food intake. (A) Schematic of FED3 in free-feeding mode. (B) Pellets per hour across six

consecutive days. Shaded areas indicate dark cycle. (C) Chronograms of pellets eaten in heatmap (top) and line

plot. (D) Regression of the calculated weight of the pellets recorded by FED vs. the measured difference in weight

of the FED between days. (E) Mice weight across time during free feeding. (F) Schematic of FED3VIZ workflow.

Data is shown as means ± SEM in (B, C, E).

The online version of this article includes the following figure supplement(s) for figure 2:

Figure supplement 1. FED3 pellet delivery cute.

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Tools and resources Neuroscience

the date and time to the storage card and dispenses another pellet. This allows for the reconstruc-

tion of detailed feeding records over multiple days. Assuming calorie content of the pellets is known,

total caloric intake can also be tracked over time. Devices were placed with ten singly housed mice

for six consecutive days with no additional food

source. We observed the expected circadian

rhythm in food intake (Figure 2B,C). To test accu-

racy, we confirmed that the daily change in

weight of the FED3 device correlated with the

number of pellets removed multiplied by the

weight of each pellet (20 mg pellets, Figure 2D).

The coefficient of determination (R2) for the

regression was 0.97, indicating an error rate

of ~3% between manual weighing and counting

of pellets by FED. Finally, we confirmed that the

mice obtained all of their necessary daily calories

from FED3, evidenced by a stable body weight

across these 6 days (Figure 2E). In other experi-

ments, we have run FED3 for >1 month without

Video 1. Example movie of mouse using FED3 in a

home-cage, and with FED3 mounted on a plastic box.

https://elifesciences.org/articles/66173#video1

Figure 3. Meal analysis with FED3. (A) Inter-pellet interval histograms for dark and light cycle feeding. (B) Meals

per day, (C) pellets per meal, and (D) % of pellets within meals for dark and light cycle feeding. N = 10 mice,

paired t-tests.

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observing weight loss. FED3 can be used for multi-week experiments like this as it has a low rate of

jamming. This is facilitated by a unique angled pellet hopper and dispensing path that has fewer pla-

ces where pellets can jam than traditional horizontal pellet dispensers (Figure 2—figure supplement

1), as well as ‘jam clearing’ movements in the code that vibrate and rotate the dispensing disk to dis-

lodge stuck pellets when they are detected. These are improvements over the first generation of

this device (Nguyen et al., 2016). As a point on rodent safety, although FED3 is resistant to jam-

ming it still must be checked for function at least once per day when being used as the only food

source.

FED3 creates a large amount of data, particularly when run over multiple days. To facilitate analy-

sis of FED3 data, we created FED3VIZ, an open-source Python-based analysis program (https://

github.com/earnestt1234/FED3_Viz). FED3VIZ offers plots for visualizing different aspects of FED3

data, including pellet retrieval, poke accuracy, pellet retrieval time, delay between consecutive pellet

earnings (inter pellet intervals), meal size, and progressive ratio breakpoint. These metrics can be

plotted for single files or group averages. FED3VIZ’s averaging methods provide options for aggre-

gating data recorded on different days, while preserving time-of day or phase of the light–dark

cycle. Furthermore, circadian activity patterns can also be visualized with the FED3VIZ ‘Chronogram’

and ‘Day/Night Plot’ functions (see Figure 7), which segment data based on a user-defined light

cycle. The processed data going into each plot can also be saved and used to create new visualiza-

tions or compute statistics in other programs, promoting reproducible, sharable analysis and visuali-

zation of FED3 data.

Meal analysisFED3 records the date and time that each pellet is removed, which can be used to quantify feeding

patterns including the size and quantity of meals, eating rate within meals, and timing between

meals. Different parameters have been used by different groups to define meals, often based on

numerical cut-offs (Farley et al., 2003; Kanoski et al., 2013; Melhorn et al., 2010). To complement

Figure 4. FED3 reveals circadian feeding patterns. (A) Schematic of FED3 in FR1 mode. (B) Active (blue) and

inactive (orange) pokes over 6 days. n = 10 mice. (C) Average pokes (active, blue; inactive, orange) over 24 hr

cycle. (D) Average pokes during light and dark cycles. Significant interaction between day/night and average

pokes (F(1,792)=85.225, p<0.0001); significant effect of day/night (F(1,792)=668.700, p<0.0001); significant effect of

active or inactive pokes (F(1,792)=610.824, p<0.0001). Fitted linear model for two-way ANOVA, (F(3,792)=454.917,

p<0.0001.) (E) Average poke efficiency (p=0.0001, student’s t-test).

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these approaches, we aimed to develop an unbiased approach for understanding meal patterns,

based on the distribution of time intervals between each pellet consumed. For the 10 mice in the

above experiment, we plotted the inter-pellet time interval histogram (Figure 3A) for both the dark

and light cycles, based on an approach that was previously described in rats (Cottone et al., 2007).

We observed a large peak at <1 min between pellets, showing that the majority of pellets were con-

sumed within 1 min of another pellet. We used this distribution to classify pellets eaten within the

same minute as belonging to the same meal, and pellets with larger intervals between them belong-

ing to different meals. This approach revealed that animals eat fewer meals during the light cycle

(Figure 3B) but eat similar number of pellets within each meal and obtain a similar fraction of their

pellets within meals in each cycle. Differences in meal patterning have been linked to obesity

(Farley et al., 2003; Wald and Grill, 2019), so this analytical approach may assist in understanding

obesity and other disorders of feeding.

Figure 5. FR1 acquisition across seven research sites. (A) Pellets earned over first 16 hr of exposure to FED3 at

seven research sites. (B) Scatterplots and kernel density estimation plots showing pellets earned after 16 hr with

FED3, effect of group (F(6,115)=6.4223, p<0.0001), significant post hoc differences between groups B and D

(p=0.001), B and G (p=0.001), C and D (p=0.030), and C and G (p=0.010). (C) Poke efficiency across the

session. Significant effect of time, p=0.0007, F(3,386)=5.79. (D) Active pokes across the session. Significant effect of

time, p=0.0001, F(3, 393)=115.2. (E) Retrieval time across the session. Significant effect of time, p=0.0001, F(3,351)

=14.02. (E) Linear models with Tukey post-tests were conducted in (C-E). (F) Poke efficiency across continuous 16

hr sessions (gray line, n = 122 mice) and across multiple days with 1 hr sessions each day (teal line, n = 11 mice).

Two-way ANOVA revealed significant effect of time (p<0.0001), no significant effect of group or interaction. F

(7,500)=3.974.

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Circadian patterns of operant behaviorA unique feature of FED3 is that it is small and wire-free, allowing it to fit inside of traditional vivar-

ium home-cages. This facilitates quantification of circadian rhythms in both operant and feeding

behavior (Figure 4A). To demonstrate operant capability, we quantified how nose-poking for pellets

varied over the circadian cycle, by running mice on a FR1 task for six consecutive days. Mice learned

to nose-poke on the active port for a single pellet. We tracked active (rewarded) and inactive (unre-

warded) pokes. As expected, both active and inactive pokes were higher during the dark cycle

(Figure 4B–D). Surprisingly, however, poke efficiency (% of total pokes on the active port) was

slightly higher during the light cycle (Figure 4E), suggesting that operant responding is more effi-

cient during the light cycle.

A multi-site study of learning rates with FED3Multiple research groups currently use FED3. We therefore asked how mouse operant learning

varies across different laboratories. To do this, we obtained operant data from the first overnight

FR1 session from seven laboratories, resulting in data from 122 mice (mix of males and females,

Figure 5A). When comparing pellets earned in an overnight session across laboratories, we

observed a significant effect of group, linked to significant differences in 4 of 21 post hoc compari-

sons (Figure 5B). When viewed as a single distribution (Figure 5B inset), the distribution of pellets

earned from all groups was consistent with a single Gaussian distribution (p>0.05, Shapiro-Wilk test

for normality). This highlights how FED3 enables high-throughput studies of operant behavior, and

the power of large sample sizes to achieve adequate sampling of a distribution of behavior

(Figure 5B).

A unique capability of FED3 is that it has a sensor for detecting the presence of the pellet in the

feeding well. This enables time-stamping of when each pellet was removed for constructing feeding

records, as well as measuring the time between the pellet dispense and removal, which we termed

‘retrieval time’. Changes in retrieval time may reflect learning, as this measure progressively

decreased as animals gained experience with FED3 (Figure 5E). Accordingly, active active count and

poke efficiency (% of pokes on the active port) increased over time, demonstrating that retrieval

time decreased on the same time scale that mice learned which poke resulted in a pellet

(Figure 5C). To compare learning rates in overnight training to traditional daily operant sessions, we

ran a new group of eleven mice for sixteen daily 1 hr FR1 sessions with FED3, returning them to the

colony for the remainder of each day. Learning rates did not differ between mice exposed to 16

daily 1 hr sessions or one 16 hr overnight session (Figure 5F). This suggests that the acquisition of

this FR1 task may depend most strongly on the cumulative time mice interact with FED3 and that

overnight training can greatly speed up task acquisition by giving them many hours of experience in

one night.

Figure 6. Effect of magazine training. (A) Schematic showing paradigm for magazine trained group (MAG) vs. no

magazine training (noMAG). (B) Active pokes over time during first exposure to FED3 FR1 between noMAG and

MAG groups. (C) Scatterplots and kernel density estimation plots showing distribution of active poke counts at 16

hr (p=0.0001). Student’s t-test. N = 146 mice.

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Tools and resources Neuroscience

The effect of magazine training on acquisition of operant behaviorBased on prior literature (Steinhauer et al., 1976), we predicted that the speed of acquisition of an

FR1 task would be further increased by prior magazine training. To test this, we exposed 24 mice to

magazine training using the free feeding FED3 paradigm for 1–3 days prior to FR1 training

(Figure 6A). This paradigm dispenses a pellet into the feeding well of FED3 and replaces it when-

ever it is removed, providing an automatic method for magazine training animals. In this way, maga-

zine training allows mice to learn the location of the food source and associate food with the sound

of the pellet dispenser operating and the pellet being dispensed, before commencing with FR1

training. We compared the performance of the 24 magazine trained mice to 122 mice from Figure 5

(which had not been magazine trained) and found that magazine training resulted in significantly

higher (~2) levels of pellet acquisition in the first night with the FR1 task (Figure 6B,C). Therefore,

magazine training is recommended to speed up acquisition of nose-poking tasks.

Figure 7. Longitudinal closed-economy feeding with FED3. (A) Active pokes required per pellet in a 4 day closed-

economy progressive ratio task with 30 min resets. (B) Inset illustrating increasing number of pokes (y-axis, and

orange rasters) per pellet earned during dark cycle. (C) Mouse weights over time (n = 7 mice). (D–F) Chronograms

(top) and bar/scatter plots (bottom) showing daily average pellets (D), pokes (E), and pokes per pellet (F) over

time, binned by 1 hr. Shaded region in chronograms indicates dark cycle. Significant increase in active pokes,

p=0.0044 (E, bottom), and pokes per pellet, p=0.0007 (F, bottom) during dark cycle. No significant difference in

pellets earned during dark cycle, p=0.8622 (D, bottom).

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Closed-economy food intake with FED3FED3 can also be used for closed-economy feeding studies, in which all food is earned by operant

nose-poking (Figure 7). Closed-economy operant tasks have been used to quantify the economic

demand for food (Bauman, 1991; Chaney and Rowland, 2008), as well as changes in economic

demand due to manipulation of the dopamine system (Beeler et al., 2010; Mourra et al., 2020).

We tested seven mice on a repeating progressive ratio task (adapted from Mourra et al., 2020), in

which the nose-poking requirement began on FR1 and increased each time a pellet was earned.

When mice refrained from poking on either port for 30 min the ratio reset to FR1 (Figure 7A,B).

Although pellets earned from this task were their only food source, mouse weights remained stable

across days, demonstrating that mice earned their entire caloric need with the task (Figure 7C).

Total pellets earned did not differ between light and dark cycles (Figure 7D). However, nose-poking

rates were higher during the dark phase of their daily cycle (Figure 7E), meaning mice performed

more pokes to earn each pellet in this phase (Figure 7F). This suggests that motivation for food pel-

lets varies over the circadian cycle such that mice are willing to work harder during the dark cycle vs.

the light cycle. When the data was visualized hourly across the circadian cycle, we further noted a ~3

hr window of enhanced nose-poking rates starting shortly after the onset of the dark cycle

(Figure 7F). This time coincides with a circadian peak in extracellular dopamine levels in the striatum

(Ferris et al., 2014), and a peak in activity levels in mice (Matikainen-Ankney et al., 2020). There-

fore, this task may be used to assay changes in accumbal dopamine function across the circadian

cycle.

Optogenetic self-stimulation with FED3FED3 was also designed to synchronize with other experimental equipment through its programma-

ble output port. To demonstrate this capability, we programmed FED3 to act as a function genera-

tor and control an LED for an intra-cranial self-stimulation task. The dorsal striatum contains two

main classes of output neurons, differentiated by their expression of the dopamine D1 or D2 recep-

tors (Gerfen et al., 1990). Dorsal striatal neurons that express the D1 receptor are highly reinforcing

when optogenetically stimulated (Kravitz et al., 2012). To demonstrate how FED3 can be used to

perform an optogenetic self-stimulation experiment, we used Cre-dependent viral expression to tar-

get channelrhodopsin-2 (ChR2) to D1R-expressing neurons in the dorsal striatum (Figure 8A). FED3

was programmed to generate and trigger a 1 s train of 20 Hz pulses of 475 nm light upon each

Figure 8. Self-stimulation of dMSNs using FED3. (A) Schematic showing Cre-dependent ChR2 injected into dorsal

medial striatum with fiber optic implanted. (B) Schematic showing FED3 operant self-stim setup. (C) Mice poked

significantly more on the active port (p=0.0002, Student’s t-test, n = 3 mice). (D) Cumulative inactive and active

pokes plotted over time for each mouse.

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active nose-poke, and the session continued until they received 75 trains of stimulation (Figure 8B).

Three mice were run on this task, resulting in significantly greater active vs inactive pokes

(Figure 8C,D), and demonstrating that FED3 can be used for optogenetic self-stimulation studies.

The programmable output is attached to an on-board digital-to-analog converter chip, meaning that

it can be programmed to output pulses of any voltage from 0 to 3 V, allowing FED3 to represent

multiple behavioral events with one output line. The timing of the BNC output port is regulated by

the SAMD21 microprocessor that has a documented pulsing precision of <1 ms.

DiscussionQuantification of food intake is necessary to understand animal models of feeding disorders, as well

as other medical conditions. To quantify food intake in rodent home-cages, we previously published

the FED version 1, an open-source, stand-alone feeding device that can fit in a rack-mounted home-

cage (Nguyen et al., 2016). FED records a time series of pellets removed, which can be used to

reconstruct feeding records. FED has been used by multiple research groups to understand feeding

(Brierley et al., 2020; Burnett et al., 2019; Chen et al., 2020; Li et al., 2019; London et al., 2018).

Since its original publication, we have redesigned the FED twice and here present the third version

of FED, which revamps our original design and adds the ability to measure operant behavior, as well

as a unique ‘angled’ pellet dispenser mechanism that is resistant to jamming, which enables long

term studies. We distributed the design for FED3 online, and it has been used to study how specific

neural circuit manipulations alter food motivation (Mazzone et al., 2020; Sciolino et al., 2019;

Vachez et al., 2021), how weight-loss alters food motivation (Matikainen-Ankney et al., 2020), and

how food motivation is altered in a stress-susceptible mouse population (Rodriguez et al., 2020).

FED3 is a stand-alone solution for home-cage operant training, enabling researchers to under-

stand not just total food intake, but also motivation for and learning about food rewards. FED3 has

several unique benefits over comparable systems including: (1) FED3 is low cost. The FED3 electron-

ics cost ~$200 and the housing is 3D printed. Even if 3D printing is done professionally this is >10�

cheaper than most commercial solutions for measuring food intake or testing operant behavior; (2)

FED3 is self-contained and fits within traditional vivarium caging, allowing for measures of true

‘home-cage’ feeding without modifying the cage. Due to its small size, it can also be placed inside

of other equipment where wires might be impractical, such as within an indirect calorimetry system;

(3) FED3 has a programmable output that allows it to easily synchronize with other equipment. Due

to wiring, connecting FED3 to external hardware likely requires FED3 to be used outside of the

home-cage, or to modify the home-cage system. This has been done by multiple labs to synchronize

the output of FED3 with fiber-photometry recordings (London et al., 2018; Mazzone et al., 2020),

electrophysiological recordings (London et al., 2018), optogenetic stimulation (Vachez et al.,

2021), and video tracking (Krynitsky et al., 2020; Li et al., 2019); and (4) FED3 is open source and

all design files and codes are freely available online. This enables users to modify the code and hard-

ware to achieve new functionality.

The purpose of this manuscript is to demonstrate the utility of FED3 for feeding research. To this

end, we demonstrate experiments that measured total food intake, operant responding, and opto-

genetic stimulation. We further highlight how the high temporal resolution enables meal pattern

analysis across multiple days. Finally, we coordinated with six other research groups to compile a

dataset of 122 mice across seven research sites, all running the same experimental FR1 program.

We observed similar patterns of acquisition across all sites, demonstrating that FED3 can be used

for multi-site research studies on feeding. Due to its low cost and open-source nature, we believe

that multi-site studies with FED3 are more feasible than with other systems.

While FED3 has many strengths, it also has limitations. One limitation is that FED3 uses internal

microSD cards to store data. While microSD cards are convenient, they are not ideal for large num-

bers of devices, where removing multiple cards can be cumbersome. Wireless data logging is a

potential solution to this problem, although there are challenges to implementing this in rodent

cages. A second limitation is that animals can ‘hoard’ pellets from FED3, as animals can remove

food without consuming it. In our experience, this seemed to be a rare trait that specific mice

engaged in (<10% of mice hoard pellets). Unfortunately, FED3 has no way to determine whether a

mouse actually consumes each pellet after removal so we recommend checking for pellet hoarding

and accounting for this in experimental conclusions. A third limitation is that granular bedding can

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be kicked into the pellet well and interfere with pellet detection. To avoid this, we recommend using

‘iso-pad’ bedding, or bedding pellets that are large enough to avoid this possibility. A fourth limita-

tion is that FED3 is not waterproof and would not be resistant to flooding, though the 3D printed

housing is sufficient to protect electronics from typical wear associated with placement in a rodent

cage. A final limitation of FED3 is that it has no way of identifying individual mice in group housed

environments, so feeding records must be collected in singly housed mice. However, FED3 is

open source, so it can be modified and improved with innovations from our group and the feeding

community, and future iterations of the device may include methods for identifying multiple mice

using radio-frequency identification (RFID) tags. Being able to run studies on group housed mice

would greatly increase throughput and allow for the study of interactions between social behavior

and feeding. We published the FED3 design as open-source and look forward to community contri-

butions and modifications to enable new functionality and overcome these limitations.

Materials and methods

Key resources table

Reagent type(species) or resource Designation Source or reference Identifiers Additional information

Strain, strain background(Mus musculus)

Mus musculus with nameC57BL/6J from IMSR.

https://www.jax.org/strain/000664

RRID:IMSR_JAX:000664

Software, algorithm Python 3.7(Anaconda Distribution)

https://www.anaconda.com/

Software, algorithm Spyder 4.1 https://github.com/spyder-ide/spyder/releases

RRID:SCR_017585, version 4.1

Software, algorithm Arduino IDE 1.8.1 https://www.arduino.cc/

Software, algorithm FED3_VIZ https://github.com/earnestt1234/FED3_Viz

Analysis package forFED3 data files

Software, algorithm TinkerCAD https://www.tinkercad.com/

Other FED3 – commerciallyassembled

https://open-ephys.org/fed3/fed3

Other FED3 – open-source https://github.com/KravitzLabDevices/FED3

Other 5TUM grain-based rodentenrichment pellets

https://www.testdiet.com/Diet-Enrichment-Products/Lab-Treat-Tablets-and-Pellets/index.html

Data and code availabilityThe FED3 device is open-source, and design files and code are freely available online at: https://

github.com/KravitzLabDevices/FED3. In addition, we have made all data and analysis code for the

figures in this paper available at https://osf.io/hwxgv/.

SubjectsOne hundred and fifty-nine C57Bl/6 mice were housed in a 12 hr light/dark cycle with ad libitum

access to food and water except where described. Mice were provided laboratory chow diet (5001

Rodent Diet; Lab Supply, Fort Worth, TX). All procedures were approved by the Animal Care and

Use Committee at Washington University in St Louis, the National Institutes of Health, Williams Col-

lege, Virginia Tech, and Monash University.

Design and construction of FED3Tutorial videos and other information on assembling FED3 are available at: https://github.com/Kra-

vitzLabDevices/FED3/wiki.

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3D designThe 3D parts for FED3 were designed with TinkerCAD (Autodesk). We have printed FED3 with an

FDM printer in PLA (Sindoh 3DWox 1), as well as with a commercial SLS process in Nylon 12 (Shape-

ways). 3D files may need to be tweaked to obtain good results on different printers, so we provide

editable design files here: https://www.tinkercad.com/things/0QaiVw7KR3Y.

ElectronicsElectronics design was completed using Autodesk Eagle version 9.3. FED3 is controlled by a com-

mercial microcontroller (Adalogger Feather M0, Adafruit). This microcontroller contains an

ATSAMD21G18 ARM M0 processor that runs at 48 MHz, 256 kB of FLASH memory, 32 kB of RAM

memory, an on-board microSD card slot for writing data, and up to 20 digital inputs/output pins for

controlling other hardware. The microcontroller also contains a battery charging circuit for charging

the internal 4400mAhr LiPo battery in FED3, which provides ~1 week of run-time between charges.

Exact battery life depends on the behavioral program and how often the mouse interacts with FED3.

Additional hardware on FED3 includes a motor for controlling the pellet delivery hopper, eight

multi-color LEDs for delivering visual stimuli, a small speaker for delivering audio stimuli, a screen for

user feedback, and three infra-red beam-break sensors. Two of these sensors are used as ‘nose-

poke’ sensors to determine when the mouse pokes, while the third is used to detect the presence

and removal of each food pellet. Finally, FED3 contains a programmable output connected to a 10-

bit digital-analog converter circuit, enabling full analog output of arbitrary voltages from 0 to 3.3 V.

This can be used to synchronize FED3 with external equipment via digital pulses or analog signals.

There are two variants of the electronics for FED3: An older design is referred to as the ‘DIY FED3’,

which can be assembled by hand using commercially available components (https://hackaday.io/

project/106885-feeding-experimentation-device-3-fed3), whereas the newer design contains more

streamlined electronics but requires professional assembly. Both designs run the same code and

have identical functionality.

FirmwareThe code for FED3 is written in the Arduino language and is fully open-source. The example code

that we provide with FED3 contains several programs covering multiple common behavioral para-

digms including free feeding, time-restricted free-feeding, FR1, and PR operant tasks, as well as an

optogenetic self-stimulation mode that delivers trains of pulses for controlling a stimulation system.

We also provide an Arduino library that simplifies control of FED3 hardware to simplify writing of

custom programs.

Behavioral testing with FED3We demonstrate multiple operational modes for FED3 to highlight a range of functionality and appli-

cations. The following programs are included with the standard FED3 code:

Free-feedingIn the free-feeding mode (Figure 2), a 20 mg pellet is dispensed into the feeding well and moni-

tored with a beam-break. When the pellet is removed, the time-stamp of removal, and the latency

to retrieve the pellet are logged to the internal microSD card and a new pellet is dispensed. For the

free-feeding data in this paper, 10 mice were singly housed and the FED3 device was placed in their

cage for 6 days on a 12/12 on/off light cycle. FED3 devices were checked for functionality each day,

but mice were otherwise undisturbed.

Fixed-ratio 1In FR1 mode, FED3 logs the time-stamps of each ‘nose-poke’ event to the internal storage. When

the mouse activates the left nose-poke, the FED3 delivers a combined auditory tone (4 kHz for 0.3 s)

and visual (all eight LEDs light in blue) stimulus and dispenses a pellet. While the pellet remains in

the well both pokes remain inactive to prohibit multiple pellets piling up in the well. When the pellet

is removed, the time-stamp of removal, and the latency to retrieve the pellet are logged. For the

data in this paper, the same 10 mice that completed the ree-feeding experiment were transitioned

to an FR1 program, and FR1 data was recorded for an additional 6 days.

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Optogenetic stimulationViral infections of male and female Drd1-Cre mice were conducted under 0.5–2.5% anesthesia on a

stereotaxic apparatus. Using a Nanoject injector, 500 nL of AAV2-DIO-hSyn-ChR2 was injected bilat-

erally (500 nL/hemisphere) in the dorsomedial striatum. Optical fibers were then implanted in the

same region. After letting ChR2 express for 4 weeks, animals were pre-trained on an FR1 schedule

for pellets overnight in their home-cage. Two days later, mice were placed in a box with a FED3

device connected to an LED driver. The active poke for this paradigm was on the opposite side to

the active poke of the pre-training session. During the self-stimulation session, when the mouse

poked on the active nose-poke, it received a 1 s train of 1 mW 475 nm light at 20 Hz. Sessions were

run for 60 min in a 550 cm2 arena.

Data analysisCSV files generated by FED3 were processed and plotted with custom python scripts (Python, ver-

sion 3.6.7, Python Software Foundation, Wilmington, DE). All data and scripts are available on Open

Science Framework (https://osf.io/hwxgv/). Visualization was also completed using FED3VIZ GUI to

generate plots. FED3VIZ was written in Python’s standard library for developing GUIs (tkinter). FED3-

VIZ is a custom open-source graphical program for analyzing FED3 data. FED3VIZ code, version his-

tory, installation instructions, and user manual are available on GitHub (https://github.com/

earnestt1234/FED3_Viz). FED3VIZ offers plotting and data output for visualizing different aspects of

FED3 data, including pellet retrieval, poke accuracy, pellet retrieval time, delay between consecutive

pellet earnings (inter-pellet intervals), meal size, and progressive ratio breakpoint. Based on inter-

pellet interval histograms (Figure 3), we defined meals as pellets eaten within 1 min of each other.

In addition, we defined a minimum size of 0.1 g (five pellets) to be counted as a meal.

StatisticsBartlett’s test for equal variances was performed; one- or two-way ANOVAs with Tukey post-tests

were used to compare groups with equal variance (p>0.05) where appropriate. Linear mixed effects

models were used to analyze groups with significantly difference variances (p<0.05). p-value<0.05

was considered significant. Statistical tests to compare means were run using statsmodels module in

python (Seabold and Perktold, 2010). Data sets are presented as mean ± SEM. Numbers of animals

per experiment is listed as n=number of animals. Linear regression was used to determine correla-

tive relationships. T-tests or Mann–Whitney U tests were used to compare the means of two groups

for parametric or nonparametric data distributions, respectively.

AcknowledgementsFunding was provided from the National Institutes of Health Intramural Research Program at NIDDK

(AVK), the Washington University Diabetes Research Center Pilot and Feasibility Grant (AVK), the

Washington University Nutrition Obesity Research Center (NORC) Pilot and Feasibility Program

(AVK), the National Institute on Drug Abuse R00DA038725 (RA), the National Institute of Neurologi-

cal Disorders and Stroke R01NS117899 (JGM), the McDonnell Center for Cellular and Molecular

Neurobiology Postdoctoral Fellowship (BAMA), the National Health and Medical Research Council

of Australia (ZBA), and the Imaging, Modeling and Engineering of Diabetic Tissues T32, NIDDK,

DK108742 (BAMA), Brain and Behavior Research Foundation (NARSAD Young Investigator Grant

27416 to AVK and 27197 to MCC), National Institutes of Health National Institute on Drug Abuse

(R21-DA047127, R01-DA049924 to MCC), Whitehall Foundation Grant (2017-12-54 to MCC) and

Rita Allen Scholar Award in Pain (to MCC).

Additional information

Competing interests

Filipe Carvalho: Director of Open Ephys Production Site. The other authors declare that no compet-

ing interests exist.

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Tools and resources Neuroscience

Funding

Funder Grant reference number Author

National Institutes of Health ZIA DK075099 Bridget A Matikainen-AnkneyMohamed AliAmy K SuttonKatrina P NguyenMichael J KrashesAlexxai V Kravitz

Washington University Dia-betes Research Center

Bridget A Matikainen-AnkneyThomas EarnestEric CaseyAlex A LegariaAlexxai V Kravitz

Washington University Nutri-tion Obesity Research Center

Kia M BarclayMakenzie R NorrisYu-Hsuan ChangMeaghan C Creed

National Institutes of Health R00DA038725 Ream Al-Hasani

National Institutes of Health R01NS117899 Jordan G McCall

McDonnell Center for SystemsNeuroscience

Bridget A Matikainen-Ankney

National Health and MedicalResearch Council

Zane B Andrews

National Institutes of Health DK108742 Bridget A Matikainen-Ankney

National Institutes of Health DA047127 Meaghan C Creed

National Institutes of Health DA049924 Meaghan C Creed

Whitehall Foundation 2017-12-54 Meaghan C Creed

Rita Allen Foundation Meaghan C Creed

Brain and Behavior ResearchFoundation

Meaghan C CreedAlexxai V Kravitz

National Institutes of Health Intramural ResearchProgram

Alexxai V Kravitz

Brain and Behavior ResearchFoundation

27416 Alexxai V Kravitz

Brain and Behavior ResearchFoundation

27197 Meaghan C Creed

The funders had no role in study design, data collection and interpretation, or the

decision to submit the work for publication.

Author contributions

Bridget A Matikainen-Ankney, Data curation, Formal analysis, Visualization, Methodology, Writing -

original draft, Writing - review and editing; Thomas Earnest, Mohamed Ali, Software, Methodology;

Eric Casey, Validation, Investigation; Justin G Wang, Victor Cazares, Michael J Krashes, Investigation,

Writing - review and editing; Amy K Sutton, Alex A Legaria, Kia M Barclay, Laura B Murdaugh, Make-

nzie R Norris, Yu-Hsuan Chang, Katrina P Nguyen, Alex Reichenbach, Rachel E Clarke, Romana Stark,

Sineadh M Conway, Ream Al-Hasani, Jordan G McCall, Matthew W Buczynski, Investigation; Eric Lin,

Filipe Carvalho, Methodology; Meaghan C Creed, Conceptualization, Investigation; Zane B Andrews,

Supervision, Project administration; Alexxai V Kravitz, Conceptualization, Resources, Data curation,

Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization,

Methodology, Writing - original draft, Project administration, Writing - review and editing

Author ORCIDs

Eric Casey http://orcid.org/0000-0003-0041-6586

Ream Al-Hasani http://orcid.org/0000-0002-8781-6234

Matikainen-Ankney, Earnest, Ali, et al. eLife 2021;10:e66173. DOI: https://doi.org/10.7554/eLife.66173 15 of 18

Tools and resources Neuroscience

Jordan G McCall http://orcid.org/0000-0001-8295-0664

Michael J Krashes http://orcid.org/0000-0003-0966-3401

Zane B Andrews http://orcid.org/0000-0002-9097-7944

Alexxai V Kravitz https://orcid.org/0000-0001-5983-0218

Ethics

Animal experimentation: 166 C57Bl/6 mice were housed in a 12-hour light/dark cycle with ad libitum

access to food and water except where described. Mice were provided laboratory chow diet (5001

Rodent Diet; Lab Supply, Fort Worth, Texas). All procedures were approved by the Animal Care and

Use Committee at Washington University in St Louis, the National Institutes of Health, Williams Col-

lege, Virginia Tech and Monash University.

Decision letter and Author response

Decision letter https://doi.org/10.7554/eLife.66173.sa1

Author response https://doi.org/10.7554/eLife.66173.sa2

Additional filesSupplementary files. Transparent reporting form

Data availability

The FED3 device is open-source and design files and code are freely available online at: https://

github.com/KravitzLabDevices/FED3. In addition, we have made all data and analysis code for this

paper available at https://osf.io/hwxgv/.

The following dataset was generated:

Author(s) Year Dataset title Dataset URLDatabase andIdentifier

Kravitz L,Matikainen-AnkneyB

2020 Feeding Experimentation Deviceversion 3 (FED3): An open-sourcedevice for measuring food intakeand motivation in rodents

https://osf.io/hwxgv/ Open ScienceFramework, hwxgv

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